AI-Powered Skill Gap Analysis and the Discovery of Teaching Coaches: Toward Evidence-Based Teacher Development
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📜 Abstract Artificial
intelligence (AI) has transformed how organizations diagnose, monitor, and
close performance gaps. In education, AI-powered skill gap analysis offers an
opportunity to revolutionize teacher evaluation and professional development
through data-driven insight. This paper explores how AI can be applied to
teaching performance evaluation and the identification of new coaching and
supervisory talent among teachers. Drawing from the work of Hotwani (2025),
Almubarak, Alhalabi, Albidewi, and Alharbi (2025), and the OECD (2024), the
discussion argues that AI systems enable precise diagnostics that personalize
teacher development, align professional learning with institutional goals,
and identify emerging leaders within teaching cohorts. The essay emphasizes
ethical implementation, institutional transparency, and the importance of
human oversight, positioning AI as a complement to, not a replacement for,
professional judgment in teacher growth and leadership development. |
📜 Keywords: Artificial Intelligence, Skill
Gap Analysis, Teacher Evaluation, Adaptive Learning, Professional Development,
Educational Leadership, AI Ethics |
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📜 Resumen La inteligencia
artificial (IA) ha transformado la forma en que las organizaciones
diagnostican, monitorean y cierran brechas de desempeño. En el ámbito
educativo, el análisis de brechas de habilidades impulsado por IA representa
una oportunidad para revolucionar la evaluación docente y el desarrollo
profesional mediante información basada en datos. Este trabajo explora cómo
la IA puede aplicarse a la evaluación del desempeño docente y a la
identificación de nuevos talentos para funciones de mentoría o supervisión.
Basándose en Hotwani (2025), Almubarak, Alhalabi, Albidewi y Alharbi (2025),
y la OCDE (2024), se argumenta que los sistemas de IA permiten diagnósticos
precisos que personalizan la formación docente, alinean el desarrollo
profesional con los objetivos institucionales e identifican futuros líderes
dentro del cuerpo docente. Se enfatiza la necesidad de una implementación
ética, la transparencia institucional y la supervisión humana, considerando
la IA como un complemento —no un sustituto— del juicio profesional en el
crecimiento y liderazgo docente. |
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📜 Resumo A inteligência
artificial (IA) transformou a forma como as organizações diagnosticam,
monitoram e reduzem lacunas de desempenho. No contexto educacional, a análise
de lacunas de competências baseada em IA oferece uma oportunidade para
revolucionar a avaliação docente e o desenvolvimento profissional com base em
dados precisos. Este estudo investiga como a IA pode ser aplicada à avaliação
do desempenho docente e à identificação de novos talentos para funções de
mentoria e supervisão. Com base em Hotwani (2025), Almubarak, Alhalabi,
Albidewi e Alharbi (2025) e na OCDE (2024), argumenta-se que os sistemas de
IA permitem diagnósticos personalizados que alinham o crescimento docente aos
objetivos institucionais e identificam potenciais líderes educacionais. O texto
destaca a importância da ética, da transparência institucional e da
supervisão humana, considerando a IA como um apoio — e não uma
substituição — ao julgamento profissional no desenvolvimento e liderança
docente. |
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Introduction
Teacher performance evaluation has traditionally
relied on classroom observations, student course outcomes, and supervisor’s
feedback and assessment. While these methods offer valuable insights, they
often suffer from subjectivity, inconsistency, and limited scope. As
educational institutions embrace data-informed practices, AI-powered skill gap analysis has emerged as a transformative tool
capable of identifying teacher competencies, performance gaps, and leadership
potential. As Hotwani (2025) explains, machine learning models now enable
organizations to “gather and analyze data from examinations, performance
evaluations, LMS records, and even work items”, providing targeted feedback and
individualized learning pathways. This approach can equally benefit educational
settings by enhancing teacher growth and helping institutions identify emerging
teaching coaches and supervisors.
Literature Review: AI in
Skill Gap and Teacher Evaluation
Recent advances in AI applications to teacher
evaluation illustrate the potential of data-driven systems to offer a more
objective, continuous, and personalized approach. Almubarak, Alhalabi,
Albidewi, and Alharbi (2025) proposed a deep-learning model capable of
analyzing classroom video data to assess teacher–student interactions,
demonstrating how such systems “provide more consistent and scalable measures
of instructional performance” (p. 2). Similarly, the AI-based Teacher Performance Evaluation System developed in Saudi
Arabia leverages algorithmic weighting of key performance indicators (KPIs) to
produce accurate, evidence-based teacher assessments (Discover Applied
Sciences, 2024).
At the level of self-evaluation, the Teacher
Artificial Intelligence Competence Self-Efficacy Scale (TAICS) provides a
validated instrument to measure teachers’ readiness and confidence in using AI
for pedagogical innovation (Education and Information Technologies, 2024).
Collectively, these frameworks establish a foundation for AI-driven diagnostics
of teaching effectiveness, while adaptive learning systems can deliver
responsive professional development aligned with detected needs.
Theoretical Basis and
Framework
AI-powered skill gap analysis in education
operates through five interrelated phases: a) data collection, b) competency
mapping, c) gap identification, d) adaptive learning, e) and iteration. Data
are drawn from multiple sources, such as classroom observations, student
evaluations, peer reviews, and LMS analytics, to feed machine learning models
that compare current performance with established teaching standards (Hotwani,
2025).
In teacher development, this process can be
visualized as a continuous loop:
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1 |
Data Collection: |
AI compiles evidence from classroom artifacts
and performance reviews. |
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2 |
Gap Mapping: |
Algorithms identify discrepancies between
observed and desired competencies. |
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3 |
Adaptive Feedback: |
Teachers receive customized professional
development content. |
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4 |
Monitoring: |
Ongoing analysis tracks growth and adjusts
learning recommendations. |
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5 |
Leadership Identification: |
Teachers demonstrating consistent excellence,
reflective capacity, and peer recognition are flagged as potential coaches or
supervisors. |
This model aligns with the concept of adaptive learning ecosystems, which
Hotwani (2025) describes as AI systems that “continuously evolve through
feedback loops,” ensuring that professional learning remains relevant and
effective.
Identifying Teaching Coaches
through AI-Driven Evaluation
One promising application of AI-powered analysis
lies in identifying teachers with high leadership or mentoring potential. Language
schools can use performance data to locate educators who demonstrate
exceptional communication, reflection, and peer collaboration. Machine learning
models may analyze variables such as peer feedback sentiment, classroom
engagement metrics, and student progress indicators to identify those whose
teaching practices positively correlate with learner outcomes.
According to the OECD (2024), AI in education
“can strengthen teacher agency and collaboration by supporting virtual coaching
and peer mentoring systems” (p. 11). When a teacher exhibits strong performance
consistency and improvement across multiple areas, AI-supported systems can
recommend them for leadership tracks or teacher coaching programs. This
automated identification process complements traditional human evaluation,
reducing bias and accelerating leadership development pipelines within schools
or institutions.
Case Studies and Applications
Empirical evidence supports the viability of
AI-powered gap analysis for education. Almubarak, Alhalabi, Albidewi, and
Alharbi (2025) demonstrated that automated image and video processing can
classify classroom events (e.g., teacher questioning, student engagement) to
provide real-time feedback. Similarly, in corporate learning contexts, Hotwani
(2025) highlighted how AI identified deficiencies in negotiation skills within
a global sales team, leading to a 22 percent increase in performance outcomes
after targeted training. This principle can easily translate to education: AI
could detect teachers’ recurring difficulties, such as diminished communication-oriented
activities, limited digital integration overlooking the SAMR principles, classroom
management challenges, or the amount of teacher talk as opposed to student
talk, and then recommend microlearning modules accordingly.
Another example is the Saudi AI-based Teacher Performance Evaluation
System (Discover Applied Sciences, 2024), which integrates human expertise
with algorithmic scoring to ensure objectivity and reliability. This model
could enable educational administrators to detect not only underperformance but
also excellence, signaling candidates for coaching or supervisory roles within
the organization or academic departments.
Ethical and Practical
Considerations
Despite its promise, AI-based evaluation systems
must address several ethical and practical issues. First, data privacy is paramount: classroom recordings, feedback, and
performance data contain sensitive information requiring strict compliance with
regulations stated by the institution aligned with the country’s laws. Second, algorithmic bias can distort results if
training data are not representative of diverse teaching styles or teaching
methodologies. Third, teacher trust and
buy-in are essential; educators must perceive AI-powered evaluations as
supportive rather than punitive. As Hotwani (2025) cautions, “human
decision-making is not replaced by machines; rather, it is enhanced by it”.
Institutions should thus pair AI analytics with human mentorship and
qualitative reflection to ensure balanced, ethical decision-making.
Implications for Educational
Leadership and Policy
Integrating AI-powered skill gap analysis into
educational systems offers long-term benefits for leadership & coaching
cultivation, institutional development, and the spotting of areas where
teachers need to continue developing themselves in terms of classroom delivery.
Administrators, on the other hand, can establish continuous professional
learning cycles, supported by adaptive AI tools that align individual teacher
growth with pedagogical principles, methodological aims, and organizational
goals. Over time, these data insights can inform promotions, coaching
assignments, and strategic hiring for key position within the institution. As
the OECD (2024) emphasizes, when used responsibly, AI can “promote equity and
inclusion in teacher development by tailoring professional learning
opportunities to each educator’s context” (p. 15). Thus, AI not only modernizes
evaluation but also democratizes access to growth and leadership pathways.
Conclusion
AI-powered skill gap analysis represents a
paradigm shift and a new growth mindset in educational performance evaluation
and leadership development. By synthesizing diverse data sources through
machine learning, institutions can pinpoint skill deficiencies, personalize
teacher training, and identify potential coaches or supervisors with unusual
accuracy. However, this transformation must proceed with careful attention to
privacy, transparency, and human oversight. Ultimately, AI should be viewed as
an ally that enhances professional growth and organizational learning rather
than a replacement for human discernment. As education in 2025 continues to
evolve, institutions that embrace AI responsibly will be best equipped to
cultivate resilient, reflective, and future-ready teaching teams.
📚 References
Almubarak, A., Alhalabi, W., Albidewi, I., & Alharbi, E. (2025). An
AI-powered framework for assessing teacher performance in classroom
interactions: A deep learning approach. Frontiers
in Artificial Intelligence, 8(1553051).
Discover Applied
Sciences. (2024). An analytical approach
for an AI-based teacher performance evaluation system in Saudi Arabia’s
schools. https://doi.org/10.1007/s42452-024-06117-4
Education and
Information Technologies. (2024). Development
and validation of the teacher artificial intelligence competence self-efficacy
(TAICS) scale. https://doi.org/10.1007/s10639-024-13094-z
Hotwani, K.
(2025). AI-powered skill gap analysis:
Tailoring custom eLearning modules to individual needs in 2025. Custom eLearning Blog. Upside Learning.
Organisation for
Economic Co-operation and Development. (2024). The potential impact of artificial intelligence on equity and inclusion
in education. OECD Publishing. https://doi.org/10.1787/15df715b-en
Conceptual Model
Visualization - Conceptual Model [Handout] by Jonathan Acuña
AI-Powered Skill Gap Analysis and the Discovery of Teaching Coaches by Jonathan Acuña
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